Stochastic gradients closely relate to both optimization and generalization of deep neural networks (DNNs). Some works attempted to explain the success of stochastic optimization for deep learning by the arguably heavy-tail properties of gradient noise, while other works presented theoretical and empirical evidence against the heavy-tail hypothesis on gradient noise. Unfortunately, formal statistical tests for analyzing the structure and heavy tails of stochastic gradients in deep learning are still under-explored. In this paper, we mainly make two contributions. First, we conduct formal statistical tests on the distribution of stochastic gradients and gradient noise across both parameters and iterations. Our statistical tests reveal that dimension-wise gradients usually exhibit power-law heavy tails, while iteration-wise gradients and stochastic gradient noise caused by minibatch training usually do not exhibit power-law heavy tails. Second, we further discover that the covariance spectra of stochastic gradients have the power-law structures in deep learning. While previous papers believed that the anisotropic structure of stochastic gradients matters to deep learning, they did not expect the gradient covariance can have such an elegant mathematical structure. Our work challenges the existing belief and provides novel insights on the structure of stochastic gradients in deep learning.

最近的隐私泄漏事件和更严格的政策法规要求公司和移动应用程序的合规标准更高。但是，此类义务还在应用程序开发人员遵守包含各种观点，活动和角色的这些法规方面面临重大挑战，尤其是对于在此问题或资源有限的小型公司和开发人员中。为了解决这些障碍，我们开发了一个自动工具NL2GDPR，该工具可以从开发人员的自然语言描述中制定策略，同时还可以确保该应用程序的功能符合通用数据保护法规（GDPR）。 NL2GDPR是通过利用由百度认知计算实验室开发的信息提取工具OIA（开放信息注释）开发的。核心，NL2GDPR是一个以隐私为中心的信息提取模型，附有GDPR策略查找器和策略生成器。我们进行一项全面的研究，以掌握提取以隐私为中心的信息和制定隐私政策的挑战，同时利用针对此特定任务的优化。借助NL2GDPR，我们可以在正确识别与个人数据存储，过程和共享类型相关的GDPR策略方面获得92.9％，95.2％和98.4％的精度。据我们所知，NL2GDPR是第一个允许开发人员自动生成GDPR策略的工具，只需要输入自然语言来描述应用程序功能。请注意，其他非GDPR相关功能可能与生成的功能集成在一起，以构建复杂的应用程序。

培训游戏强化学习代理需要与环境进行多次互动。无知的随机探索可能会导致浪费时间和资源。减轻这种浪费至关重要。正如本文所述，在非政策演员评论家算法的设置下，我们证明，评论家可以带来更多的预期折扣奖励，而不是至少与演员相等。因此，评论家预测的Q值是一个更好的信号，可以重新分发最初从演员预测的政策分布中采样的动作。本文介绍了新的评论家指导行动重新分布（CGAR）算法，并在Openai Mujoco任务上进行了测试。实验结果表明，我们的方法提高了样本效率并实现最先进的性能。我们的代码可以在https://github.com/tairanhuang/cgar上找到。

众所周知，深度损失景观的黑森州对深度学习的优化，概括甚至稳健性至关重要。最近的著作从经验上发现，深度学习中的Hessian Spectrum具有两个组成的结构，该结构由少数大型特征值和大量近零特征值组成。但是，Hessian频谱背后的理论机制或数学基本上仍未探索。据我们所知，我们是第一个证明训练有素的深度神经网络的黑石谱展示了简单的强力结构。受统计物理理论和天然蛋白质的光谱分析的启发，我们提供了一种最大的内部理论解释，以解释为什么幂律结构存在并暗示蛋白质演化和深神经网络训练之间的光谱平行。通过有助于广泛的实验，我们进一步使用幂律频谱框架作为探索深度学习的多种新型行为的有用工具。

生成对抗网络（GAN）具有许多潜在的医学成像应用，包括数据扩展，域适应和模型解释。由于图形处理单元（GPU）的记忆力有限，因此在低分辨率的医学图像上对当前的3D GAN模型进行了训练，因此这些模型要么无法扩展到高分辨率，要么容易出现斑驳的人工制品。在这项工作中，我们提出了一种新颖的端到端GAN体系结构，可以生成高分辨率3D图像。我们通过使用训练和推理之间的不同配置来实现这一目标。在训练过程中，我们采用了层次结构，该结构同时生成图像的低分辨率版本和高分辨率图像的随机选择子量。层次设计具有两个优点：首先，对高分辨率图像训练的记忆需求在子量之间摊销。此外，将高分辨率子体积固定在单个低分辨率图像上可确保子量化之间的解剖一致性。在推断期间，我们的模型可以直接生成完整的高分辨率图像。我们还将具有类似层次结构的编码器纳入模型中，以从图像中提取特征。 3D胸CT和脑MRI的实验表明，我们的方法在图像生成中的表现优于最新技术。我们还证明了所提出的模型在数据增强和临床相关特征提取中的临床应用。

Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt). TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy. Through implementing experiments on two real-world datasets from distinct fields, the proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.

As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially on simulating a text-guided style with both the appearance and the geometry altered simultaneously. In this paper, we present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a simple text prompt. Unlike previous approaches that either lack sufficient geometry deformations and texture details or require meshes to guide the stylization, our method can shift a 3D scene to the target style characterized by desired geometry and appearance variations without any mesh guidance. This is achieved by introducing a novel global-local contrastive learning strategy, combined with the directional constraint to simultaneously control both the trajectory and the strength of the target style. Moreover, we adopt a weight regularization method to effectively suppress cloudy artifacts and geometry noises which arise easily when the density field is transformed during geometry stylization. Through extensive experiments on various styles, we demonstrate that our method is effective and robust regarding both single-view stylization quality and cross-view consistency. The code and more results can be found in our project page: https://cassiepython.github.io/nerfart/.

In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse scenarios with few labeled samples. Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes. Furthermore, over-squashing of information is caused by the negative curvature that formed by the non-uniformity distribution and strong clustering in complex graph. To address these challenges, this paper presents a novel method named Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation (HG-MDA). For the problem of heterogeneity of information in DA, node and topology augmentation strategies are proposed for the characteristics of heterogeneous graph. And meta-relation-based attention is applied as one of the indexes for selecting augmented nodes and edges. For the problem of over-squashing of information, triangle based edge adding and removing are designed to alleviate the negative curvature and bring the gain of topology. Finally, the loss function consists of the cross-entropy loss for labeled data and the consistency regularization for unlabeled data. In order to effectively fuse the prediction results of various DA strategies, the sharpening is used. Existing experiments on public datasets, i.e., ACM, DBLP, OGB, and industry dataset MB show that HG-MDA outperforms current SOTA models. Additionly, HG-MDA is applied to user identification in internet finance scenarios, helping the business to add 30% key users, and increase loans and balances by 3.6%, 11.1%, and 9.8%.

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-quality images or videos for scenes that are constructed by R3DMs. One promising solution would be representing real-world objects as Neural Fields such as NeRFs, which are able to generate photo-realistic renderings of an object under desired viewpoints. However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows. To solve this dilemma, we propose a lighting transfer network (LighTNet) to bridge NFR and PBR, such that they can benefit from each other. LighTNet reasons about a simplified image composition model, remedies the uneven surface issue caused by R3DMs, and is empowered by several perceptual-motivated constraints and a new Lab angle loss which enhances the contrast between lighting strength and colors. Comparisons demonstrate that LighTNet is superior in synthesizing impressive lighting, and is promising in pushing NFR further in practical 3D modeling workflows. Project page: https://3d-front-future.github.io/LighTNet .

基于骨架的动作识别会受到越来越多的关注，因为骨架表示通过消除与动作无关的视觉信息来减少训练数据的量。为了进一步提高样本效率，为基于骨架的动作识别而开发了基于元学习的一局学习解决方案。这些方法根据实例级全局平均嵌入之间的相似性找到最近的邻居。但是，由于对局部不变和嘈杂特征的广义学习不足，这种测量具有不稳定的代表性，而直觉上，更细粒度的识别通常依赖于确定关键的局部身体运动。为了解决这一限制，我们介绍了自适应的局部成分感知图卷积网络，该网络将比较指标替换为相似性测量的集中之和，以对对齐的局部局部嵌入行动至关重要的空间/时间段。 NTU-RGB+D 120公共基准的全面单发实验表明，我们的方法比全球嵌入提供了更强的表示，并有助于我们的模型达到最新的最新能力。